A semantic textual similarity measurement model based on the syntactic-semantic representation

摘要

Measuring semantic textual similarity (STS) lies at the core of many applications in natural language processing (NLP). Recently, most models have considered semantic information or syntactic information, but seldom an unified model to make full use of these two kinds of information. Based on the knowledge from the trained word vectors, this paper proposes a semantic-embedded dependency tree (SEDT) model based on word2vec and glove, which can be treated as a syntactic-semantic representation. In consideration of the words in a sentence for the contribution of the semantic are different, this model extends the semantic-embedded dependency tree model to an enhanced semantic-embedded dependency tree (ESEDT). And a modified partial tree kernel (MPTK) is proposed to automatically extract the syntactic-semantic patterns in this tree. Because the syntactic information, semantic knowledge, and the contribution distribution of the word attention model are all considered in this model, it can measure more comprehensive sentence semantics to improve the accuracy of STS results. Finally, SEDT/E-SEDT is applied to SemEval semantic textual similarity tasks and evaluate its performance through two widely used benchmarks: the Pearson correlation coefficient and the Spearman correlation coefficient. The experimental results show that SEDT/E-SEDT can effectively improve the accuracies of sentence similarity judgments. Compared with the other similar methods to calculate the semantic similarity, such as some neural network models, SEDT/E-SEDT can obtain better performance on most dataset.